Boost the performance of the data science team
Data scientists’ time is expensive. Don’t waste it on non-differentiated work and avoid reinventing the wheel by creating machine learning platforms from scratch. A good ML platform will support the machine learning lifecycle from data ingestion to model serving and monitoring, increase the productivity of data analysts by 10x, support machine learning software and frameworks, enable automated machine learning, and let you scale the team more efficiently.
Increase the quality of ML decisions
The cost of errors in machine learning is getting higher as companies increasingly rely on closed-loop systems. Implementing model testing, data quality, model monitoring, and anomaly detection decreases the chances of production issues and facilitates high-quality insights.
Consistently deliver actionable insights
DevOps and Continuous Delivery became standard in application development long ago. But the core principles of DevOps can be expanded to the machine learning process within your business. With the right platform you can further increase efficiency with automated machine learning and by providing necessary machine learning algorithms and frameworks including deep learning and automl.
Deploy in the cloud
Using the cloud to enable new machine learning use cases is the simplest way to begin the cloud journey for data analytics. Migrate or deploy a new cloud platform to increase the agility and productivity of the data science team. Use it as a prototype for the larger cloud migration and let the data gravity shift to the cloud over time.
Make data-driven decisions at the edge
Some companies have significant infrastructure at the edge. Factories, stores, branches, distribution centers, gas stations, and a variety of IoT use cases may take advantage of deploying machine learning models locally to lower latency and make decisions without internet connectivity. These companies can take advantage of open source-based infrastructure agnostic data science platforms to make decisions in real-time at the edge.
How to choose and implement a machine learning platform?
We have developed advanced artificial intelligence use cases, machine learning platforms, and onboard MLOps processes for Fortune-1000 enterprises across various industries including telecom, retail, media, gaming, and financial services.
Accelerate your journey to AI
We provide flexible engagement options to design and build ML platforms and artificial intelligence use cases, and onboard the MLOps process and culture. Contact us today to get started with a workshop, discovery, or PoC.
We offer free half-day workshops with our top experts in ML platforms and MLOps and real-time analytics to discuss your stream processing strategy, challenges, optimization opportunities, and industry best practices.
If you have already identified a need to improve the machine learning process and onboard an ML platform, we can start with a 4–8-week proof-of-concept project to deliver tangible results for your enterprise.
If you’re at the requirements analysis stage, we can start with a 2–3-week discovery phase to identify the current challenges, perform gap analysis, design your solution, and build an implementation and training roadmap.